cancer_benchmark: Gene expression datasets for benchmarking classification...
In jonalim/mfBiclust: Pipelines for Bi-Clustering Using Matrix Factorization
Description
Usage
Format
Details
Source
Description
Pre-classified microarray and RNAseq datasets from human cancer samples,
originally published by de Souto MC, Costa IG, de Araujo DS, Ludermir TB,
Schliep A in 2008.
Usage
1
Format
A list of 13 datasets where each element contains:
- data
a dataframe of expression values, unmodified from biclustlib
- labels
a binary matrix where rows are pre-classified samples and
columns are named cancer categories
Details
Redistributed from biclustlib: A Python library of biclustering algorithms
and evaluation measures. Copyright (C) 2017 Victor Alexandre Padilha.
Source
https://github.com/padilha/biclustlib/tree/master/
jonalim/mfBiclust documentation built on May 4, 2019, 4:13 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Format Details Source
Description
Pre-classified microarray and RNAseq datasets from human cancer samples, originally published by de Souto MC, Costa IG, de Araujo DS, Ludermir TB, Schliep A in 2008.
Usage
1 |
Format
A list of 13 datasets where each element contains:
- data
a dataframe of expression values, unmodified from biclustlib
- labels
a binary matrix where rows are pre-classified samples and columns are named cancer categories
Details
Redistributed from biclustlib: A Python library of biclustering algorithms and evaluation measures. Copyright (C) 2017 Victor Alexandre Padilha.
Source
https://github.com/padilha/biclustlib/tree/master/
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.